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无尿细胞DNA检测平台用于细菌囊炎诊断,通过基于点的机器学习

Yan Liu¹, Kai Xiao², Ziping Wu²

¹Department of Urology, Hohhot First Hospital, Hohhot 010030, China; School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, Guangdong 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangdong 510006, China.

Journal of advanced research

|November 28, 2025

概括

一个新的机器学习系统只需20分钟就能快速检测出细菌性囊炎,使用尿路无细胞DNA (cfDNA). 这种创新方法为细菌感染的诊断提供了高准确度,改进了传统方法.

科学领域:

生物医学工程生物医学工程
传染病诊断传染病诊断传染病诊断
分子诊断学分子诊断

背景情况:

细菌性囊炎,通常是由大肠杆菌引起的,需要快速识别才能有效治疗.
无尿细胞DNA (cfDNA) 是一个有前途的生物标志物,但区分细菌和非细菌的原因是具有挑战性的.

研究的目的:

开发基于图像的,机器学习辅助的光检测系统,用于同时进行尿液cfDNA的定量和定性分析.
为了能够快速准确地诊断细菌性囊炎.

主要方法:

使用的聚乙烯胺 (PEI) 功能化的玻璃幻灯片用于cfDNA丰富.
采用光成像与酸标记和YOLOv5对象检测算法进行自动分析.
使用细菌性囊炎小鼠模型和148个人类尿样验证了该系统.

主要成果:

该平台在20分钟内量化了cfDNA,检测极限为20.29 ng/mL.
对cfDNA斑点模式的自动分析区分了细菌性 (77.12%较小的斑点) 与非细菌性囊炎 (13.87%较小的斑点).
在临床样本中实现了94.43%的诊断准确度,94.45%的灵敏度和94.35%的细菌囊炎特异性,显著优于传统方法.

结论:

这种快速的现场平台不需要尿液预处理,提升了细菌囊炎的临床检测 (POCT).
该系统在各种炎症和传染病中对数字生物标志物检测有潜在的应用.

关键词:

细菌性囊炎是一种细菌性囊炎.没有细胞的DNA.图片图片图片图片图片机器学习是机器学习.尿道尿道尿道

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